Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties

Buildings with spaces requiring strict temperature and humidity controls, such as pharmaceutical cleanrooms and semiconductor/microchip factories, have been growing very quickly in terms of total floor area and energy consumption. In such buildings, much of the energy is unnecessarily wasted due to the incoordination of system design and operation/control, especially under "off-design" and ever-changing ambient and load conditions. This paper, therefore, proposes a probabilistic optimal design method for cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties. To consider the effects of asynchronous loads in different zones/spaces with reduced computation demand, a probabilistic diversity factor method is proposed which is a simplified method to quantify the effects of uncertainties of space load diversities in multiple zones/spaces using diversity factors. The proposed design method is implemented and validated in the design optimization of air-conditioning systems for implementing four different ventilation control strategies considering possible and uncertain off-design conditions. The energy and economic performance as well as service satisfaction of the air-conditioning systems are also evaluated and compared. Results show that the proposed design method can obtain the optimal air-conditioning systems with minimum life-cycle cost and superior satisfaction of service.